Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms
Authors: Alexander Camuto, George Deligiannidis, Murat A. Erdogdu, Mert Gurbuzbalaban, Umut Simsekli, Lingjiong Zhu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For modern neural networks, we develop an efficient algorithm to compute the developed bound and support our theory with various experiments on neural networks. |
| Researcher Affiliation | Academia | 1: University of Oxford & Alan Turing Institute 2: University of Toronto & Vector Institute 3: Rutgers Business School 4: INRIA & École Normale Supérieure PSL Research University 5: Florida State University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available at https://github.com/umutsimsekli/fractal_generalization. |
| Open Datasets | Yes | trained on CIFAR10, SVHN and Boston House Prices (BHP). |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section S7 in the Supplementary Document |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Section S7 in the Supplementary Document |
| Software Dependencies | No | The paper mentions 'Py Hessian' as an external codebase used but does not provide specific version numbers for all key software dependencies. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section S7 in the Supplementary Document |